S-Graphs 2.0 -- A Hierarchical-Semantic Optimization and Loop Closure for SLAM

TL;DR

S-Graphs 2.0 introduces a hierarchical semantic approach for indoor SLAM that improves optimization efficiency and accuracy by leveraging scene structure and floor-level semantics, especially in multi-floor environments.

Contribution

The paper presents a novel hierarchical scene graph representation with floor detection and a floor-based loop closure strategy for more efficient and accurate multi-floor SLAM.

Findings

Achieves state-of-the-art accuracy in multi-floor environments.

Estimates hierarchical maps up to 10x faster than baselines.

Effectively rejects false positive loop closures across floors.

Abstract

The hierarchical structure of 3D scene graphs shows a high relevance for representations purposes, as it fits common patterns from man-made environments. But, additionally, the semantic and geometric information in such hierarchical representations could be leveraged to speed up the optimization and management of map elements and robot poses. In this direction, we present our work Situational Graphs 2.0 (S-Graphs 2.0), which leverages the hierarchical structure of indoor scenes for efficient data management and optimization. Our algorithm begins by constructing a situational graph that represents the environment into four layers: Keyframes, Walls, Rooms, and Floors. Our first novelty lies in the front-end, which includes a floor detection module capable of identifying stairways and assigning floor-level semantic relations to the underlying layers. Floor-level semantics allows us to propose a floor-based loop closure strategy, that effectively rejects false positive closures that typically appear due to aliasing between different floors of a building. Our second novelty lies in leveraging our representation hierarchy in the optimization. Our proposal consists of: (1) local optimization over a window of recent keyframes and their connected components across the four representation layers, (2) floor-level global optimization, which focuses only on keyframes and their connections within the current floor during loop closures, and (3) room-level local optimization, marginalizing redundant keyframes that share observations within the room, which reduces the computational footprint. We validate our algorithm extensively in different real multi-floor environments. Our approach shows state-of-art-art accuracy metrics in large-scale multi-floor environments, estimating hierarchical representations up to 10x faster, in average, than competing baselines